{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T20:39:21Z","timestamp":1773520761651,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the \u2018Outstanding Innovation Scholarship for Doctoral Candidate of CUMT\u2019","award":["2019YCBS054"],"award-info":[{"award-number":["2019YCBS054"]}]},{"name":"the \u2018Outstanding Innovation Scholarship for Doctoral Candidate of CUMT\u2019","award":["2022M723378"],"award-info":[{"award-number":["2022M723378"]}]},{"name":"the \u2018Outstanding Innovation Scholarship for Doctoral Candidate of CUMT\u2019","award":["80NSSC20K0365"],"award-info":[{"award-number":["80NSSC20K0365"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2019YCBS054"],"award-info":[{"award-number":["2019YCBS054"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M723378"],"award-info":[{"award-number":["2022M723378"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["80NSSC20K0365"],"award-info":[{"award-number":["80NSSC20K0365"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NASA LCLUC","award":["2019YCBS054"],"award-info":[{"award-number":["2019YCBS054"]}]},{"name":"NASA LCLUC","award":["2022M723378"],"award-info":[{"award-number":["2022M723378"]}]},{"name":"NASA LCLUC","award":["80NSSC20K0365"],"award-info":[{"award-number":["80NSSC20K0365"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The leaf area index (LAI) is a vital parameter for quantifying the material and energy exchange between terrestrial ecosystems and the atmosphere. The Global Ecosystem Dynamics Investigation (GEDI), with its mission to produce a near-global map of forest structure, provides a product of the effective leaf area index (referred to as GEDI LAIe). However, it is unclear about the performance of GEDI LAIe across different temperate forest types and the degree of factors influencing GEDI LAIe performance. This study assessed the accuracy of GEDI LAIe in temperate forests and quantifies the effects of various factors, such as the difference of gap fraction (DGF) between GEDI and discrete point cloud Lidar of the National Ecological Observatory Network (NEON), sensor system parameters, and characteristics of the canopy, topography, and soil. The reference data for the LAIe assessment were derived from the NEON discrete point cloud Lidar, referred to as NEON Lidar LAIe, covering 12 forest types across 22 sites in the Continental United States (the CONUS). Results showed that GEDI underestimated LAIe (Bias: \u22120.56 m2\/m2), with values of the mean absolute error (MAE), root mean square error (RMSE), percent bias (%Bias), and percent RMSE (%RMSE) of 0.70 m2\/m2, 0.89 m2\/m2, \u22120.20, and 0.31, respectively. Among forest types, the underestimation of GEDI LAIe in broadleaf forests and mixed forests was generally greater than that in coniferous forests, which showed a moderate error (%RMSE: 0.33~0.52). Factor analysis indicated that multiple factors explained 52% variance of the GEDI LAIe error, among which the DGF contributed the most with a relative importance of 49.82%, followed by characteristics of canopy and soil with a relative importance of 23.20% and 16.18%, respectively. The DGF was a key pivot for GEDI LAIe error; that is, other factors indirectly influence the GEDI LAIe error by affecting the DGF first. Our findings demonstrated that the GEDI LAIe product has good performance, and the factor analysis is expected to shed some light on further improvements in GEDI LAIe estimation.<\/jats:p>","DOI":"10.3390\/rs15061535","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Accuracy Assessment and Impact Factor Analysis of GEDI Leaf Area Index Product in Temperate Forest"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7119-8358","authenticated-orcid":false,"given":"Cangjiao","family":"Wang","sequence":"first","affiliation":[{"name":"Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Duo","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Shaogang","family":"Lei","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8503-8053","authenticated-orcid":false,"given":"Izaya","family":"Numata","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, 1021 Medary Ave, Wecota Hall Box 506B, Brookings, SD 57007, USA"}]},{"given":"Luo","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China"},{"name":"Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/j.1365-3040.1992.tb00992.x","article-title":"Defining leaf area index for non-flat leaves","volume":"15","author":"Chen","year":"1992","journal-title":"Plant. Cell Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4259","DOI":"10.1038\/s41467-019-12257-8","article-title":"Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink","volume":"10","author":"Chen","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6863","DOI":"10.5194\/gmd-15-6863-2022","article-title":"FORCCHN V2.0: An individual-based model for predicting multiscale forest carbon dynamics","volume":"15","author":"Fang","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/S0034-4257(00)00211-X","article-title":"Light transmittance in forest canopies determined using airborne laser altimetry and in-canopy quantum measurements","volume":"76","author":"Parker","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.rse.2005.03.005","article-title":"Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems","volume":"96","author":"Hyde","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.1109\/JSTARS.2016.2569469","article-title":"Indirect measurement of forest leaf area index using path length distribution model and multispectral canopy imager","volume":"9","author":"Hu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.rse.2013.12.007","article-title":"Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA","volume":"143","author":"Tang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.agrformet.2018.11.033","article-title":"Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives","volume":"265","author":"Yan","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108101","DOI":"10.1016\/j.agrformet.2020.108101","article-title":"An assessment study of three indirect methods for estimating leaf area density and leaf area index of individual trees","volume":"292\u2013293","author":"Wei","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.rse.2012.05.005","article-title":"Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at la selva, costa rica","volume":"124","author":"Tang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.rse.2014.08.007","article-title":"Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS\/ICESat)","volume":"154","author":"Tang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cui, L., Guo, J., Jiao, Z., Sun, M., Dong, Y., Zhang, X., Yin, S., Chang, Y., DIng, A., and Xie, R. (August, January 28). Retrieval of the Forest Leaf Area Index Based on the Laser Penetration Ratio from the GLAS Waveform Lidar Data. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898057"},{"key":"ref_14","first-page":"102488","article-title":"Leaf area index retrieval with ICESat-2 photon counting LiDAR","volume":"103","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth\u2019s forests and topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cui, L., Jiao, Z., Zhao, K., Sun, M., Dong, Y., and Yin, S. (2020). Index Using Transmitted Energy Information Derived from ICESat GLAS Data. Remote Sens., 12.","DOI":"10.3390\/rs12152457"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1016\/j.rse.2010.02.021","article-title":"Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model","volume":"115","author":"Yang","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9745","DOI":"10.1109\/TGRS.2021.3054324","article-title":"Footprint Size Design of Large-Footprint Full-Waveform LiDAR for Forest and Topography Applications: A Theoretical Study","volume":"59","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"743320","DOI":"10.3389\/frsen.2021.743320","article-title":"Airborne and Spaceborne Lidar Reveal Trends and Patterns of Functional Diversity in a Semi-Arid Ecosystem","volume":"2","author":"Ilangakoon","year":"2021","journal-title":"Front. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dhargay, S., Lyell, C.S., Brown, T.P., Inbar, A., Sheridan, G.J., and Lane, P.N.J. (2022). Performance of GEDI Space-Borne LiDAR for Quantifying Structural Variation in the Temperate Forests of South-Eastern Australia. Remote Sens., 14.","DOI":"10.3390\/rs14153615"},{"key":"ref_21","unstructured":"Tang, H., and Armston, J. (2019). Algorithm Theoretical Basis Document (ATBD) for GEDI L2B Footprint Canopy Cover and Vertical Profile Metrics, Goddard Space Flight Center."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1109\/36.951085","article-title":"Modeling lidar waveforms in heterogeneous and discrete canopies","volume":"39","author":"Jupp","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rishmawi, K., Huang, C., Schleeweis, K., and Zhan, X. (2022). Integration of VIIRS Observations with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from 2013 to 2020 across the Conterminous United States. Remote Sens., 14.","DOI":"10.3390\/rs14102320"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Boucher, P.B., Hancock, S., Orwig, D.A., Duncanson, L., Armston, J., Tang, H., Krause, K., Cook, B., Paynter, I., and Li, Z. (2020). Detecting change in forest structure with simulated GEDI lidarwaveforms: A case study of the hemlock woolly adelgid (HWA; adelges tsugae) infestation. Remote Sens., 12.","DOI":"10.3390\/rs12081304"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sanchez-Lopez, N., Boschetti, L., Hudak, A.T., Hancock, S., and Duncanson, L.I. (2020). Estimating time since the last stand-replacing disturbance (TSD) from spaceborne simulated GEDI data: A feasibility study. Remote Sens., 12.","DOI":"10.3390\/rs12213506"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, C., Elmore, A.J., Numata, I., Cochrane, M.A., Lei, S., Hakkenberg, C.R., Li, Y., Zhao, Y., and Tian, Y. (2022). A Framework for Improving Wall-to-Wall Canopy Height Mapping by Integrating GEDI LiDAR. Remote Sens., 14.","DOI":"10.3390\/rs14153618"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1890\/1540-9295(2007)5[59:NAHDNE]2.0.CO;2","article-title":"NEON: A hierarchically designed national ecological network","volume":"5","author":"Schimel","year":"2007","journal-title":"Front. Ecol. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e03640","DOI":"10.1002\/ecs2.3640","article-title":"Leveraging the NEON Airborne Observation Platform for socio-environmental systems research","volume":"12","author":"Ordway","year":"2021","journal-title":"Ecosphere"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1111\/geb.13380","article-title":"Climate mediates the relationship between plant biodiversity and forest structure across the United States","volume":"30","author":"Hakkenberg","year":"2021","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1080\/15481603.2022.2085354","article-title":"Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA","volume":"59","author":"Wang","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112571","DOI":"10.1016\/j.rse.2021.112571","article-title":"Performance evaluation of GEDI and ICESat-2 laser altimeter data for terrain and canopy height retrievals","volume":"264","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112383","DOI":"10.1016\/j.rse.2021.112383","article-title":"A data-driven approach to estimate leaf area index for Landsat images over the contiguous US","volume":"258","author":"Kang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_33","first-page":"4406220","article-title":"Clumping Effects in Leaf Area Index Retrieval from Large-Footprint Full-Waveform LiDAR","volume":"60","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"12386","DOI":"10.1109\/JSTARS.2021.3130738","article-title":"Correcting Crown-Level Clumping Effect for Improving Leaf Area Index Retrieval from Large-Footprint LiDAR: A Study Based on the Simulated Waveform and GLAS Data","volume":"14","author":"Jiang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2708904","DOI":"10.34133\/2021\/2708904","article-title":"Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies","volume":"2021","author":"Yan","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.rse.2013.02.018","article-title":"Investigating assumptions of crown archetypes for modelling LiDAR returns","volume":"134","author":"Calders","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.rse.2016.07.010","article-title":"Simulation of satellite, airborne and terrestrial LiDAR with DART (I): Waveform simulation with quasi-Monte Carlo ray tracing","volume":"184","author":"Yin","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.jhydrol.2012.03.026","article-title":"Responses of evapotranspiration at different topographic positions and catchment water balance following a pronounced drought in a mixed species eucalypt forest, Australia","volume":"440\u2013441","author":"Mitchell","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2005.05.003","article-title":"Global mapping of foliage clumping index using multi-angular satellite data","volume":"97","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2008.09.007","article-title":"Full-waveform topographic lidar: State-of-the-art","volume":"64","author":"Mallet","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Fang, H. (2020). Estimation of LAI with the LiDAR technology: A review. Remote Sens., 12.","DOI":"10.3390\/rs12203457"},{"key":"ref_42","unstructured":"Luthcke, S.B., Rebold, T., Thomas, T., and Pennington, T. (2019). Algorithm Theoretical Basis Document (ATBD) for GEDI Waveform Geolocation for L1 and L2 Products, Goddard Space Flight Center."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"1","article-title":"Global Ecosystem Dynamics Investigation (GEDI) Level 1B User Guide For SDPS PGEVersion 3 (P003) of GEDI L1B Data Science Team","volume":"3","author":"Hansen","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1029\/1999GL010484","article-title":"Modeling laser altimeter return waveforms over complex vegetation using high-resolution elevation data","volume":"26","author":"Blair","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI Simulator: A Large-Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions","volume":"6","author":"Hancock","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_47","unstructured":"NEON (National Ecological Observatory Network) (2022, December 25). Discrete Return LiDAR Point Cloud (DP1.30003.001), RELEASE-2022. Available online: https:\/\/data.neonscience.org."},{"key":"ref_48","unstructured":"NEON (National Ecological Observatory Network) (2023, January 31). Digital Hemispheric Photos of Plot Vegetation (DP1.10017.001), RELEASE-2023. Available online: https:\/\/data.neonscience.org."},{"key":"ref_49","unstructured":"NEON (National Ecological Observatory Network) (2022, December 25). Elevation\u2014LiDAR (DP3.30024.001), RELEASE-2022. Available online: https:\/\/data.neonscience.org."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. Remote Sens., 11.","DOI":"10.1101\/532952"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101061","DOI":"10.1016\/j.ecoinf.2020.101061","article-title":"Cross-site learning in deep learning RGB tree crown detection","volume":"56","author":"Weinstein","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1111\/2041-210X.13472","article-title":"DeepForest: A Python package for RGB deep learning tree crown delineation","volume":"11","author":"Weinstein","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_53","unstructured":"Weiss, M., and Baret, F. (2017). CAN_EYE V6.4.91 User Manual, HAL Open Science."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.agrformet.2004.02.005","article-title":"Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests","volume":"124","author":"Valladares","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, L., Akerblom, M., Wang, T., Skidmore, A., Zhu, X., and Heurich, M. (2021). Comparative evaluation of algorithms for leaf area index estimation from digital hemispherical photography through virtual forests. Remote Sens., 13.","DOI":"10.3390\/rs13163325"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.agrformet.2003.08.001","article-title":"Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling","volume":"121","author":"Weiss","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2007.08.021","article-title":"Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information","volume":"112","author":"Blackard","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hengl, T., De Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagoti\u0107, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global Gridded Soil Information Based on Machine Learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tian, L., Qu, Y., and Qi, J. (2021). Estimation of forest lai using discrete airborne lidar: A review. Remote Sens., 13.","DOI":"10.3390\/rs13122408"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2013.02.021","article-title":"Direct retrieval of canopy gap probability using airborne waveform lidar","volume":"134","author":"Armston","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.isprsjprs.2005.12.001","article-title":"Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner","volume":"60","author":"Wagner","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","first-page":"1","article-title":"Relative importance for linear regression in R: The package relaimpo","volume":"17","year":"2006","journal-title":"J. Stat. Softw."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e02864","DOI":"10.1002\/ecy.2864","article-title":"High rates of primary production in structurally complex forests","volume":"100","author":"Gough","year":"2019","journal-title":"Ecology"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e2021JG006748","DOI":"10.1029\/2021JG006748","article-title":"Unraveling Forest Complexity: Resource Use Efficiency, Disturbance, and the Structure-Function Relationship","volume":"127","author":"Murphy","year":"2022","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/36.387593","article-title":"Quantifying the Effect of Canopy Architecture on Optical Measurements of Leaf Area Index Using Two Gap Size Analysis Methods","volume":"33","author":"Chen","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TGRS.2018.2794504","article-title":"Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index","volume":"56","author":"Hu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.isprsjprs.2018.12.010","article-title":"Retrieving leaf area index in discontinuous forest using ICESat\/GLAS full-waveform data based on gap fraction model","volume":"148","author":"Yang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1080\/2150704X.2013.790573","article-title":"Retrieving leaf area index using ICESat\/GLAS full-waveform data","volume":"4","author":"Luo","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"239","DOI":"10.5194\/bg-13-239-2016","article-title":"Characterizing leaf area index (LAI) and vertical foliage profile (VFP) over the United States","volume":"13","author":"Tang","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/JSTARS.2016.2557074","article-title":"Differentiating Tree and Shrub LAI in a Mixed Forest with ICESat\/GLAS Spaceborne LiDAR","volume":"10","author":"Tian","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_71","first-page":"G00E11","article-title":"Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing","volume":"115","author":"Lee","year":"2010","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Mahoney, C., Hopkinson, C., Kljun, N., and van Gorsel, E. (2017). Estimating canopy gap fraction using ICESat GLAS within Australian forest ecosystems. Remote Sens., 9.","DOI":"10.3390\/rs9010059"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2013.05.020","article-title":"A comparison of foliage profiles in the Sierra National Forest obtained with a full-waveform under-canopy EVI lidar system with the foliage profiles obtained with an airborne full-waveform LVIS lidar system","volume":"136","author":"Zhao","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"100024","DOI":"10.1016\/j.srs.2021.100024","article-title":"The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring","volume":"4","author":"Roy","year":"2021","journal-title":"Sci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:52:28Z","timestamp":1760122348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":74,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061535"],"URL":"https:\/\/doi.org\/10.3390\/rs15061535","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]}}}